Introduction to Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: TMBD Movie Analysis\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "<li><a href=\"#limitations\">Limitations</a></li>\n",
    "</ul>\n",
    "\n",
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "In this project we will clarify some correlations for the movies that are released between 1960 and 2015. Votes, budgets and revenues will be considered in the data analyze."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "### General Properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data(arg):\n",
    "    df = pd.read_csv(arg)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>homepage</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>...</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>32.99</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>http://www.jurassicworld.com/</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>...</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>6/9/15</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.50</td>\n",
       "      <td>2015</td>\n",
       "      <td>137999939.28</td>\n",
       "      <td>1392445892.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76341</td>\n",
       "      <td>tt1392190</td>\n",
       "      <td>28.42</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>http://www.madmaxmovie.com/</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>What a Lovely Day.</td>\n",
       "      <td>...</td>\n",
       "      <td>An apocalyptic story set in the furthest reach...</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Village Roadshow Pictures|Kennedy Miller Produ...</td>\n",
       "      <td>5/13/15</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.10</td>\n",
       "      <td>2015</td>\n",
       "      <td>137999939.28</td>\n",
       "      <td>348161292.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id    imdb_id  popularity     budget     revenue      original_title  \\\n",
       "0  135397  tt0369610       32.99  150000000  1513528810      Jurassic World   \n",
       "1   76341  tt1392190       28.42  150000000   378436354  Mad Max: Fury Road   \n",
       "\n",
       "                                                cast  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "\n",
       "                        homepage         director             tagline  ...  \\\n",
       "0  http://www.jurassicworld.com/  Colin Trevorrow   The park is open.  ...   \n",
       "1    http://www.madmaxmovie.com/    George Miller  What a Lovely Day.  ...   \n",
       "\n",
       "                                            overview runtime  \\\n",
       "0  Twenty-two years after the events of Jurassic ...     124   \n",
       "1  An apocalyptic story set in the furthest reach...     120   \n",
       "\n",
       "                                      genres  \\\n",
       "0  Action|Adventure|Science Fiction|Thriller   \n",
       "1  Action|Adventure|Science Fiction|Thriller   \n",
       "\n",
       "                                production_companies release_date vote_count  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...       6/9/15       5562   \n",
       "1  Village Roadshow Pictures|Kennedy Miller Produ...      5/13/15       6185   \n",
       "\n",
       "   vote_average  release_year   budget_adj   revenue_adj  \n",
       "0          6.50          2015 137999939.28 1392445892.52  \n",
       "1          7.10          2015 137999939.28  348161292.49  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data('tmdb-movies.csv')\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10866, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   id                    10866 non-null  int64  \n",
      " 1   imdb_id               10856 non-null  object \n",
      " 2   popularity            10866 non-null  float64\n",
      " 3   budget                10866 non-null  int64  \n",
      " 4   revenue               10866 non-null  int64  \n",
      " 5   original_title        10866 non-null  object \n",
      " 6   cast                  10790 non-null  object \n",
      " 7   homepage              2936 non-null   object \n",
      " 8   director              10822 non-null  object \n",
      " 9   tagline               8042 non-null   object \n",
      " 10  keywords              9373 non-null   object \n",
      " 11  overview              10862 non-null  object \n",
      " 12  runtime               10866 non-null  int64  \n",
      " 13  genres                10843 non-null  object \n",
      " 14  production_companies  9836 non-null   object \n",
      " 15  release_date          10866 non-null  object \n",
      " 16  vote_count            10866 non-null  int64  \n",
      " 17  vote_average          10866 non-null  float64\n",
      " 18  release_year          10866 non-null  int64  \n",
      " 19  budget_adj            10866 non-null  float64\n",
      " 20  revenue_adj           10866 non-null  float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'imdb_id', 'popularity', 'budget', 'revenue', 'original_title',\n",
       "       'cast', 'homepage', 'director', 'tagline', 'keywords', 'overview',\n",
       "       'runtime', 'genres', 'production_companies', 'release_date',\n",
       "       'vote_count', 'vote_average', 'release_year', 'budget_adj',\n",
       "       'revenue_adj'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  1. We can get rid of unuseful specific data columns to make a clean data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['id', 'imdb_id', 'cast', 'tagline', 'keywords', 'homepage', 'overview', 'production_companies','budget_adj','revenue_adj', 'genres', 'release_date'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.99</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>124</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.50</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.42</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>120</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.10</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity     budget     revenue      original_title         director  \\\n",
       "0       32.99  150000000  1513528810      Jurassic World  Colin Trevorrow   \n",
       "1       28.42  150000000   378436354  Mad Max: Fury Road    George Miller   \n",
       "\n",
       "   runtime  vote_count  vote_average  release_year  \n",
       "0      124        5562          6.50          2015  \n",
       "1      120        6185          7.10          2015  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10866, 9)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   popularity      10866 non-null  float64\n",
      " 1   budget          10866 non-null  int64  \n",
      " 2   revenue         10866 non-null  int64  \n",
      " 3   original_title  10866 non-null  object \n",
      " 4   director        10822 non-null  object \n",
      " 5   runtime         10866 non-null  int64  \n",
      " 6   vote_count      10866 non-null  int64  \n",
      " 7   vote_average    10866 non-null  float64\n",
      " 8   release_year    10866 non-null  int64  \n",
      "dtypes: float64(2), int64(5), object(2)\n",
      "memory usage: 764.1+ KB\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. There are some null values in director column.\n",
    "it would be hard and timetaking to specify for each row by searching movies on web."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0040493281796429225\n"
     ]
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
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       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Iliza Shlesinger: Freezing Hot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71</td>\n",
       "      <td>14</td>\n",
       "      <td>6.60</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sense8: Creating the World</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>7.50</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>With This Ring</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105</td>\n",
       "      <td>14</td>\n",
       "      <td>6.50</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Marvel Studios: Assembling a Universe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43</td>\n",
       "      <td>32</td>\n",
       "      <td>6.30</td>\n",
       "      <td>2014</td>\n",
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       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unlocking Sherlock</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>7.20</td>\n",
       "      <td>2014</td>\n",
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       "      <th>1203</th>\n",
       "      <td>0.12</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>Free to Play</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>40</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2014</td>\n",
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       "      <th>1241</th>\n",
       "      <td>0.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Dance-Off</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>5.70</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Top Gear: The Perfect Road Trip 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>12</td>\n",
       "      <td>6.80</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>The Diary of Anne Frank</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>19</td>\n",
       "      <td>7.50</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>0.09</td>\n",
       "      <td>3250000</td>\n",
       "      <td>0</td>\n",
       "      <td>Paa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133</td>\n",
       "      <td>11</td>\n",
       "      <td>6.10</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1895</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Doctor Who: The Waters of Mars</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62</td>\n",
       "      <td>25</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2221</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Scott Pilgrim vs. the Animation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>7.70</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bo Burnham: Words, Words, Words</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>13</td>\n",
       "      <td>6.50</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Across the Line: The Exodus of Charlie Wright</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>11</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2315</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Listen to Your Heart</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>7.30</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2318</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Barbie in A Mermaid Tale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>35</td>\n",
       "      <td>6.30</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2376</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Doctor Who: A Christmas Carol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62</td>\n",
       "      <td>11</td>\n",
       "      <td>7.70</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2397</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>The Making of The Walking Dead</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>42</td>\n",
       "      <td>8.40</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Opeth: In Live Concert At The Royal Albert Hall</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163</td>\n",
       "      <td>10</td>\n",
       "      <td>8.60</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2403</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yu-Gi-Oh! 3D: Bonds Beyond Time</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3171</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>The Assassination of Jesse James: Death Of An ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>20</td>\n",
       "      <td>6.80</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3224</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>John Mayer: Where the Light Is Live in Los Ang...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>164</td>\n",
       "      <td>16</td>\n",
       "      <td>8.50</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3276</th>\n",
       "      <td>0.15</td>\n",
       "      <td>4180000</td>\n",
       "      <td>11000000</td>\n",
       "      <td>Kismat Konnection</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153</td>\n",
       "      <td>11</td>\n",
       "      <td>5.80</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3285</th>\n",
       "      <td>0.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Tropic Thunder: Rain of Madness</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>6.90</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3357</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bill Burr: Why Do I Do This?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55</td>\n",
       "      <td>10</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3365</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Foo Fighters: Live at Wembley Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120</td>\n",
       "      <td>10</td>\n",
       "      <td>8.40</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3369</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Wizards On Deck With Hannah Montana</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "      <td>14</td>\n",
       "      <td>6.10</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3910</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Steve Jobs: One Last Thing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>6.60</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4679</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Barbie in A Mermaid Tale 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74</td>\n",
       "      <td>22</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4797</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Doctor Who: The Snowmen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>7.80</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4872</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Party Bercy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120</td>\n",
       "      <td>15</td>\n",
       "      <td>6.40</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4939</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>The Men Who Built America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360</td>\n",
       "      <td>11</td>\n",
       "      <td>5.30</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5413</th>\n",
       "      <td>0.32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>The Brave Little Toaster to the Rescue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5866</th>\n",
       "      <td>0.21</td>\n",
       "      <td>5000000</td>\n",
       "      <td>0</td>\n",
       "      <td>Jinxed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>19</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5915</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Phineas and Ferb: Mission Marvel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44</td>\n",
       "      <td>13</td>\n",
       "      <td>5.90</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5972</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Top Gear: The Perfect Road Trip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84</td>\n",
       "      <td>35</td>\n",
       "      <td>7.60</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6033</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Russell Brand: Messiah Complex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>12</td>\n",
       "      <td>6.80</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>North and South, Book I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>561</td>\n",
       "      <td>17</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7579</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>La hora frÃ­a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92</td>\n",
       "      <td>10</td>\n",
       "      <td>4.90</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7767</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Doctor Who: The Runaway Bride</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>18</td>\n",
       "      <td>7.60</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7814</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transformers: Beginnings</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>5.80</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9593</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Peter Pan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52</td>\n",
       "      <td>28</td>\n",
       "      <td>6.60</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10386</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>The Making of 'The Nightmare Before Christmas'</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>7.50</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10426</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Magical Mystery Tour</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55</td>\n",
       "      <td>15</td>\n",
       "      <td>5.80</td>\n",
       "      <td>1967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       popularity   budget   revenue  \\\n",
       "532          0.13        0         0   \n",
       "548          0.11        0         0   \n",
       "556          0.10        0         0   \n",
       "1032         0.29        0         0   \n",
       "1054         0.27        0         0   \n",
       "1203         0.12   150000         0   \n",
       "1241         0.14        0         0   \n",
       "1288         0.04        0         0   \n",
       "1852         0.26        0         0   \n",
       "1872         0.09  3250000         0   \n",
       "1895         0.06        0         0   \n",
       "2221         0.28        0         0   \n",
       "2286         0.21        0         0   \n",
       "2290         0.20        0         0   \n",
       "2315         0.17        0         0   \n",
       "2318         0.17        0         0   \n",
       "2376         0.07        0         0   \n",
       "2397         0.03        0         0   \n",
       "2401         0.07        0         0   \n",
       "2403         0.07        0         0   \n",
       "3171         0.02        0         0   \n",
       "3224         0.22        0         0   \n",
       "3276         0.15  4180000  11000000   \n",
       "3285         0.14        0         0   \n",
       "3357         0.04        0         0   \n",
       "3365         0.00        0         0   \n",
       "3369         0.00        0         0   \n",
       "3910         0.00        0         0   \n",
       "4679         0.42        0         0   \n",
       "4797         0.17        0         0   \n",
       "4872         0.09        0         0   \n",
       "4939         0.00        0         0   \n",
       "5413         0.32        0         0   \n",
       "5866         0.21  5000000         0   \n",
       "5915         0.17        0         0   \n",
       "5972         0.17        0         0   \n",
       "6033         0.05        0         0   \n",
       "6181         0.00        0         0   \n",
       "7579         0.44        0         0   \n",
       "7767         0.13        0         0   \n",
       "7814         0.04        0         0   \n",
       "9593         0.00        0         0   \n",
       "10386        0.12        0         0   \n",
       "10426        0.11        0         0   \n",
       "\n",
       "                                          original_title director  runtime  \\\n",
       "532                       Iliza Shlesinger: Freezing Hot      NaN       71   \n",
       "548                           Sense8: Creating the World      NaN       25   \n",
       "556                                       With This Ring      NaN      105   \n",
       "1032               Marvel Studios: Assembling a Universe      NaN       43   \n",
       "1054                                  Unlocking Sherlock      NaN       60   \n",
       "1203                                        Free to Play      NaN       75   \n",
       "1241                                           Dance-Off      NaN        0   \n",
       "1288                   Top Gear: The Perfect Road Trip 2      NaN       94   \n",
       "1852                             The Diary of Anne Frank      NaN      100   \n",
       "1872                                                 Paa      NaN      133   \n",
       "1895                      Doctor Who: The Waters of Mars      NaN       62   \n",
       "2221                     Scott Pilgrim vs. the Animation      NaN        4   \n",
       "2286                     Bo Burnham: Words, Words, Words      NaN       63   \n",
       "2290       Across the Line: The Exodus of Charlie Wright      NaN       94   \n",
       "2315                                Listen to Your Heart      NaN        0   \n",
       "2318                            Barbie in A Mermaid Tale      NaN       75   \n",
       "2376                       Doctor Who: A Christmas Carol      NaN       62   \n",
       "2397                      The Making of The Walking Dead      NaN       30   \n",
       "2401     Opeth: In Live Concert At The Royal Albert Hall      NaN      163   \n",
       "2403                     Yu-Gi-Oh! 3D: Bonds Beyond Time      NaN       60   \n",
       "3171   The Assassination of Jesse James: Death Of An ...      NaN       32   \n",
       "3224   John Mayer: Where the Light Is Live in Los Ang...      NaN      164   \n",
       "3276                                   Kismat Konnection      NaN      153   \n",
       "3285                     Tropic Thunder: Rain of Madness      NaN       30   \n",
       "3357                        Bill Burr: Why Do I Do This?      NaN       55   \n",
       "3365               Foo Fighters: Live at Wembley Stadium      NaN      120   \n",
       "3369                 Wizards On Deck With Hannah Montana      NaN       68   \n",
       "3910                          Steve Jobs: One Last Thing      NaN       60   \n",
       "4679                          Barbie in A Mermaid Tale 2      NaN       74   \n",
       "4797                             Doctor Who: The Snowmen      NaN       60   \n",
       "4872                                         Party Bercy      NaN      120   \n",
       "4939                           The Men Who Built America      NaN      360   \n",
       "5413              The Brave Little Toaster to the Rescue      NaN       74   \n",
       "5866                                              Jinxed      NaN       75   \n",
       "5915                    Phineas and Ferb: Mission Marvel      NaN       44   \n",
       "5972                     Top Gear: The Perfect Road Trip      NaN       84   \n",
       "6033                      Russell Brand: Messiah Complex      NaN       99   \n",
       "6181                             North and South, Book I      NaN      561   \n",
       "7579                                       La hora frÃ­a      NaN       92   \n",
       "7767                       Doctor Who: The Runaway Bride      NaN       60   \n",
       "7814                            Transformers: Beginnings      NaN       22   \n",
       "9593                                           Peter Pan      NaN       52   \n",
       "10386     The Making of 'The Nightmare Before Christmas'      NaN       25   \n",
       "10426                               Magical Mystery Tour      NaN       55   \n",
       "\n",
       "       vote_count  vote_average  release_year  \n",
       "532            14          6.60          2015  \n",
       "548            12          7.50          2015  \n",
       "556            14          6.50          2015  \n",
       "1032           32          6.30          2014  \n",
       "1054           11          7.20          2014  \n",
       "1203           40          7.00          2014  \n",
       "1241           18          5.70          2014  \n",
       "1288           12          6.80          2014  \n",
       "1852           19          7.50          2009  \n",
       "1872           11          6.10          2009  \n",
       "1895           25          8.00          2009  \n",
       "2221           19          7.70          2010  \n",
       "2286           13          6.50          2010  \n",
       "2290           11          6.00          2010  \n",
       "2315           29          7.30          2010  \n",
       "2318           35          6.30          2010  \n",
       "2376           11          7.70          2010  \n",
       "2397           42          8.40          2010  \n",
       "2401           10          8.60          2010  \n",
       "2403           10          6.00          2010  \n",
       "3171           20          6.80          2008  \n",
       "3224           16          8.50          2008  \n",
       "3276           11          5.80          2008  \n",
       "3285           12          6.90          2008  \n",
       "3357           10          8.00          2008  \n",
       "3365           10          8.40          2008  \n",
       "3369           14          6.10          2008  \n",
       "3910           11          6.60          2011  \n",
       "4679           22          6.00          2012  \n",
       "4797           10          7.80          2012  \n",
       "4872           15          6.40          2012  \n",
       "4939           11          5.30          2012  \n",
       "5413           11          7.00          1997  \n",
       "5866           19          7.00          2013  \n",
       "5915           13          5.90          2013  \n",
       "5972           35          7.60          2013  \n",
       "6033           12          6.80          2013  \n",
       "6181           17          6.00          1985  \n",
       "7579           10          4.90          2007  \n",
       "7767           18          7.60          2007  \n",
       "7814           34          5.80          2007  \n",
       "9593           28          6.60          1988  \n",
       "10386          15          7.50          1993  \n",
       "10426          15          5.80          1967  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perc = df.director.isna().sum()/df.shape[0]\n",
    "print(perc)\n",
    "df[df.director.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Since the null values are only 0.4% part of the dataframe, we can drop them.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10822 entries, 0 to 10865\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   popularity      10822 non-null  float64\n",
      " 1   budget          10822 non-null  int64  \n",
      " 2   revenue         10822 non-null  int64  \n",
      " 3   original_title  10822 non-null  object \n",
      " 4   director        10822 non-null  object \n",
      " 5   runtime         10822 non-null  int64  \n",
      " 6   vote_count      10822 non-null  int64  \n",
      " 7   vote_average    10822 non-null  float64\n",
      " 8   release_year    10822 non-null  int64  \n",
      "dtypes: float64(2), int64(5), object(2)\n",
      "memory usage: 845.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.99</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>124</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.50</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.42</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>120</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.10</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity     budget     revenue      original_title         director  \\\n",
       "0       32.99  150000000  1513528810      Jurassic World  Colin Trevorrow   \n",
       "1       28.42  150000000   378436354  Mad Max: Fury Road    George Miller   \n",
       "\n",
       "   runtime  vote_count  vote_average  release_year  \n",
       "0      124        5562          6.50          2015  \n",
       "1      120        6185          7.10          2015  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We have total of 10822 row and 10 columns in the final clean data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10822.00</td>\n",
       "      <td>10822.00</td>\n",
       "      <td>10822.00</td>\n",
       "      <td>10822.00</td>\n",
       "      <td>10822.00</td>\n",
       "      <td>10822.00</td>\n",
       "      <td>10822.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.65</td>\n",
       "      <td>14684003.70</td>\n",
       "      <td>39984216.68</td>\n",
       "      <td>102.13</td>\n",
       "      <td>218.20</td>\n",
       "      <td>5.97</td>\n",
       "      <td>2001.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.00</td>\n",
       "      <td>30962371.25</td>\n",
       "      <td>117213806.05</td>\n",
       "      <td>30.87</td>\n",
       "      <td>576.65</td>\n",
       "      <td>0.93</td>\n",
       "      <td>12.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1960.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>90.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>5.40</td>\n",
       "      <td>1995.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2006.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.72</td>\n",
       "      <td>15300000.00</td>\n",
       "      <td>24314635.25</td>\n",
       "      <td>111.00</td>\n",
       "      <td>146.00</td>\n",
       "      <td>6.60</td>\n",
       "      <td>2011.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32.99</td>\n",
       "      <td>425000000.00</td>\n",
       "      <td>2781505847.00</td>\n",
       "      <td>900.00</td>\n",
       "      <td>9767.00</td>\n",
       "      <td>9.20</td>\n",
       "      <td>2015.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       popularity       budget       revenue  runtime  vote_count  \\\n",
       "count    10822.00     10822.00      10822.00 10822.00    10822.00   \n",
       "mean         0.65  14684003.70   39984216.68   102.13      218.20   \n",
       "std          1.00  30962371.25  117213806.05    30.87      576.65   \n",
       "min          0.00         0.00          0.00     0.00       10.00   \n",
       "25%          0.21         0.00          0.00    90.00       17.00   \n",
       "50%          0.39         0.00          0.00    99.00       38.00   \n",
       "75%          0.72  15300000.00   24314635.25   111.00      146.00   \n",
       "max         32.99 425000000.00 2781505847.00   900.00     9767.00   \n",
       "\n",
       "       vote_average  release_year  \n",
       "count      10822.00      10822.00  \n",
       "mean           5.97       2001.30  \n",
       "std            0.93         12.82  \n",
       "min            1.50       1960.00  \n",
       "25%            5.40       1995.00  \n",
       "50%            6.00       2006.00  \n",
       "75%            6.60       2011.00  \n",
       "max            9.20       2015.00  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's have a wide look to data histograms.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(20,10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Which years have highest and lowest movie releases? Compare the average votes in these years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**According to histogram above , it looks like the highest movie releases is between years 2009-2015. Firstly, lets check the years in 'release_year' column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n",
       "       1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n",
       "       1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n",
       "       1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n",
       "       2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n",
       "       2015], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years = df.release_year.unique()\n",
    "years.sort()\n",
    "years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Our data consists of movies that are released between years 1960 and 2015. Let's check the released movie counts per years.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['release_year'].value_counts()[:].plot(kind='bar', figsize = (16,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now we can point out the highest and lowest counts for 2014 and 1969.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "695 31\n"
     ]
    }
   ],
   "source": [
    "rel_max = df['release_year'].value_counts().max()\n",
    "rel_min = df['release_year'].value_counts().min()\n",
    "print(rel_max , rel_min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now we can calculate average votes in these years.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "vote_2014 = df.query('release_year == 2014').vote_average.mean()\n",
    "vote_1969 = df.query('release_year == 1969').vote_average.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average vote of the year with highest movie realeases is  5.915827338129501\n",
      "The average vote of the year with lowest movie realeases is  5.961290322580646\n"
     ]
    }
   ],
   "source": [
    "print(\"The average vote of the year with highest movie realeases is \", vote_2014)\n",
    "print(\"The average vote of the year with lowest movie realeases is \", vote_1969)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [vote_2014,vote_1969]\n",
    "x = [rel_max, rel_min]\n",
    "plt.plot(x,y);\n",
    "plt.xlabel('Count of Released Movies');\n",
    "plt.ylabel('Vote average');\n",
    "plt.title('The Correlation of Released Movies Count and Average Votes in 1969 and 2014');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The amount of released movies are higher in 2014; however, the average votes are higher in 1969. It can be said that there is a negative correlation in years 2014 and 1969.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We are comparing the highest and lowest years but it would be better if we also check the wide look around the years for voting.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('release_year')['vote_average'].mean().plot(figsize=(12,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Except the 1970-1975 period, our findings are matching. Moving average would show a better graph which is suitable with our results but the graph already support our findings.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. What is the relationship of revenues of movies by their budgets? What are the properties of the movie with highest revenue?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's dive in our data for making some predictions to answer the question.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('release_year')['budget'].mean().plot(figsize=(12,6), kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In the last decade, the technology in movies improved;furthermore, expenses increased. There might be an issue for the last 10 years in our data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
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       "      <th>30</th>\n",
       "      <td>3.93</td>\n",
       "      <td>0</td>\n",
       "      <td>29355203</td>\n",
       "      <td>Mr. Holmes</td>\n",
       "      <td>Bill Condon</td>\n",
       "      <td>103</td>\n",
       "      <td>425</td>\n",
       "      <td>6.40</td>\n",
       "      <td>2015</td>\n",
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       "      <td>3.36</td>\n",
       "      <td>0</td>\n",
       "      <td>22354572</td>\n",
       "      <td>Solace</td>\n",
       "      <td>Afonso Poyart</td>\n",
       "      <td>101</td>\n",
       "      <td>474</td>\n",
       "      <td>6.20</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2.27</td>\n",
       "      <td>0</td>\n",
       "      <td>45895</td>\n",
       "      <td>Beyond the Reach</td>\n",
       "      <td>Jean-Baptiste LÃ©onetti</td>\n",
       "      <td>95</td>\n",
       "      <td>81</td>\n",
       "      <td>5.50</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mythica: The Darkspore</td>\n",
       "      <td>Anne K. Black</td>\n",
       "      <td>108</td>\n",
       "      <td>27</td>\n",
       "      <td>5.10</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Me and Earl and the Dying Girl</td>\n",
       "      <td>Alfonso Gomez-Rejon</td>\n",
       "      <td>105</td>\n",
       "      <td>569</td>\n",
       "      <td>7.70</td>\n",
       "      <td>2015</td>\n",
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       "      <th>...</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Carry On Screaming!</td>\n",
       "      <td>Gerald Thomas</td>\n",
       "      <td>87</td>\n",
       "      <td>13</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1966</td>\n",
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       "    <tr>\n",
       "      <th>10861</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>The Endless Summer</td>\n",
       "      <td>Bruce Brown</td>\n",
       "      <td>95</td>\n",
       "      <td>11</td>\n",
       "      <td>7.40</td>\n",
       "      <td>1966</td>\n",
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       "      <th>10862</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Grand Prix</td>\n",
       "      <td>John Frankenheimer</td>\n",
       "      <td>176</td>\n",
       "      <td>20</td>\n",
       "      <td>5.70</td>\n",
       "      <td>1966</td>\n",
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       "      <td>0</td>\n",
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       "      <td>94</td>\n",
       "      <td>11</td>\n",
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       "      <td>1966</td>\n",
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       "      <td>0.06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>What's Up, Tiger Lily?</td>\n",
       "      <td>Woody Allen</td>\n",
       "      <td>80</td>\n",
       "      <td>22</td>\n",
       "      <td>5.40</td>\n",
       "      <td>1966</td>\n",
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       "<p>5656 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "       popularity  budget   revenue                  original_title  \\\n",
       "30           3.93       0  29355203                      Mr. Holmes   \n",
       "36           3.36       0  22354572                          Solace   \n",
       "72           2.27       0     45895                Beyond the Reach   \n",
       "74           2.17       0         0          Mythica: The Darkspore   \n",
       "75           2.14       0         0  Me and Earl and the Dying Girl   \n",
       "...           ...     ...       ...                             ...   \n",
       "10860        0.09       0         0             Carry On Screaming!   \n",
       "10861        0.08       0         0              The Endless Summer   \n",
       "10862        0.07       0         0                      Grand Prix   \n",
       "10863        0.07       0         0             Beregis Avtomobilya   \n",
       "10864        0.06       0         0          What's Up, Tiger Lily?   \n",
       "\n",
       "                      director  runtime  vote_count  vote_average  \\\n",
       "30                 Bill Condon      103         425          6.40   \n",
       "36               Afonso Poyart      101         474          6.20   \n",
       "72     Jean-Baptiste LÃ©onetti       95          81          5.50   \n",
       "74               Anne K. Black      108          27          5.10   \n",
       "75         Alfonso Gomez-Rejon      105         569          7.70   \n",
       "...                        ...      ...         ...           ...   \n",
       "10860            Gerald Thomas       87          13          7.00   \n",
       "10861              Bruce Brown       95          11          7.40   \n",
       "10862       John Frankenheimer      176          20          5.70   \n",
       "10863           Eldar Ryazanov       94          11          6.50   \n",
       "10864              Woody Allen       80          22          5.40   \n",
       "\n",
       "       release_year  \n",
       "30             2015  \n",
       "36             2015  \n",
       "72             2015  \n",
       "74             2015  \n",
       "75             2015  \n",
       "...             ...  \n",
       "10860          1966  \n",
       "10861          1966  \n",
       "10862          1966  \n",
       "10863          1966  \n",
       "10864          1966  \n",
       "\n",
       "[5656 rows x 9 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('budget == 0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**With a quick research on web I found out that many of movies with zero budget has an actual budget.**\n",
    "\n",
    "**There are some missing information in the data. Using mean or a presumptive calculation might cause a false deduction. So I would like to use rows which have budget and revenue columns are different from 0.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
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       "      <td>28.42</td>\n",
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       "      <td>Mad Max: Fury Road</td>\n",
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       "      <th>2</th>\n",
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       "      <td>295238201</td>\n",
       "      <td>Insurgent</td>\n",
       "      <td>Robert Schwentke</td>\n",
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       "      <td>6.30</td>\n",
       "      <td>2015</td>\n",
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       "      <th>3</th>\n",
       "      <td>11.17</td>\n",
       "      <td>200000000</td>\n",
       "      <td>2068178225</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>J.J. Abrams</td>\n",
       "      <td>136</td>\n",
       "      <td>5292</td>\n",
       "      <td>7.50</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.34</td>\n",
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       "      <td>Furious 7</td>\n",
       "      <td>James Wan</td>\n",
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       "      <td>0.67</td>\n",
       "      <td>7500000</td>\n",
       "      <td>33736689</td>\n",
       "      <td>Who's Afraid of Virginia Woolf?</td>\n",
       "      <td>Mike Nichols</td>\n",
       "      <td>131</td>\n",
       "      <td>74</td>\n",
       "      <td>7.50</td>\n",
       "      <td>1966</td>\n",
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       "    <tr>\n",
       "      <th>10828</th>\n",
       "      <td>0.40</td>\n",
       "      <td>3000000</td>\n",
       "      <td>13000000</td>\n",
       "      <td>Torn Curtain</td>\n",
       "      <td>Alfred Hitchcock</td>\n",
       "      <td>128</td>\n",
       "      <td>46</td>\n",
       "      <td>6.30</td>\n",
       "      <td>1966</td>\n",
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       "    <tr>\n",
       "      <th>10829</th>\n",
       "      <td>0.40</td>\n",
       "      <td>4653000</td>\n",
       "      <td>6000000</td>\n",
       "      <td>El Dorado</td>\n",
       "      <td>Howard Hawks</td>\n",
       "      <td>120</td>\n",
       "      <td>36</td>\n",
       "      <td>6.90</td>\n",
       "      <td>1966</td>\n",
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       "      <td>0.30</td>\n",
       "      <td>12000000</td>\n",
       "      <td>20000000</td>\n",
       "      <td>The Sand Pebbles</td>\n",
       "      <td>Robert Wise</td>\n",
       "      <td>182</td>\n",
       "      <td>28</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1966</td>\n",
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       "      <th>10848</th>\n",
       "      <td>0.21</td>\n",
       "      <td>5115000</td>\n",
       "      <td>12000000</td>\n",
       "      <td>Fantastic Voyage</td>\n",
       "      <td>Richard Fleischer</td>\n",
       "      <td>100</td>\n",
       "      <td>42</td>\n",
       "      <td>6.70</td>\n",
       "      <td>1966</td>\n",
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       "</table>\n",
       "<p>3854 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "       popularity     budget     revenue                   original_title  \\\n",
       "0           32.99  150000000  1513528810                   Jurassic World   \n",
       "1           28.42  150000000   378436354               Mad Max: Fury Road   \n",
       "2           13.11  110000000   295238201                        Insurgent   \n",
       "3           11.17  200000000  2068178225     Star Wars: The Force Awakens   \n",
       "4            9.34  190000000  1506249360                        Furious 7   \n",
       "...           ...        ...         ...                              ...   \n",
       "10822        0.67    7500000    33736689  Who's Afraid of Virginia Woolf?   \n",
       "10828        0.40    3000000    13000000                     Torn Curtain   \n",
       "10829        0.40    4653000     6000000                        El Dorado   \n",
       "10835        0.30   12000000    20000000                 The Sand Pebbles   \n",
       "10848        0.21    5115000    12000000                 Fantastic Voyage   \n",
       "\n",
       "                director  runtime  vote_count  vote_average  release_year  \n",
       "0        Colin Trevorrow      124        5562          6.50          2015  \n",
       "1          George Miller      120        6185          7.10          2015  \n",
       "2       Robert Schwentke      119        2480          6.30          2015  \n",
       "3            J.J. Abrams      136        5292          7.50          2015  \n",
       "4              James Wan      137        2947          7.30          2015  \n",
       "...                  ...      ...         ...           ...           ...  \n",
       "10822       Mike Nichols      131          74          7.50          1966  \n",
       "10828   Alfred Hitchcock      128          46          6.30          1966  \n",
       "10829       Howard Hawks      120          36          6.90          1966  \n",
       "10835        Robert Wise      182          28          7.00          1966  \n",
       "10848  Richard Fleischer      100          42          6.70          1966  \n",
       "\n",
       "[3854 rows x 9 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gain = df.query('revenue != 0 & budget != 0 ')\n",
    "df_gain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now let's try to check budget per year.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_gain.groupby('release_year')['budget'].mean().plot(figsize=(12,6), kind = 'bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**After the correction we can confirm that there were a lot of missing budget values in the data for the last 10 years. Now it isn't curved to the right.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_gain.groupby('release_year')['revenue'].mean().plot(figsize=(12,6), kind = 'bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We have 2 different graphs but it would be better if we put them in one graph to point out the results.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_gain.groupby('release_year')['budget'].mean().plot(figsize = (16,8),alpha = 1 , kind = 'bar',color = 'blue', label = 'budget')\n",
    "df_gain.groupby('release_year')['revenue'].mean().plot(figsize = (16,8),alpha = 0.5 , kind = 'bar', color = 'orange', label = 'revenue')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Budget & Revenue')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It looks like there is a positivite correlation between revenue and budget.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2781505847"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.revenue.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>9.43</td>\n",
       "      <td>237000000</td>\n",
       "      <td>2781505847</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>162</td>\n",
       "      <td>8458</td>\n",
       "      <td>7.10</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      popularity     budget     revenue original_title       director  \\\n",
       "1386        9.43  237000000  2781505847         Avatar  James Cameron   \n",
       "\n",
       "      runtime  vote_count  vote_average  release_year  \n",
       "1386      162        8458          7.10          2009  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(' revenue == 2781505847 ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In the last 2 cells, we determined the movie with a highest revenue which is Avatar. Let's make a brief from our findings.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "The amount of released movies in ***2014*** is ***695*** movie. It is ***31*** movie in ***1969***.\n",
    "\n",
    "The average vote of the year with highest movie releases (***2014***) is  ***5.92***, lowest movie releases (***1969***) is  ***5.96***.\n",
    "\n",
    "There is a positive correlation between budget and revenue.\n",
    "\n",
    "Avatar has the highest revenue (***2.781.505.847 $***). The director of the movie is James Cameron. It is released on ***2009*** and the vote average is ***7.10***."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='limitations'></a>\n",
    "## Limitations\n",
    "\n",
    "***This study has some limitations.***\n",
    "\n",
    "**1.** Around 7k rows from raw data hasn't any value on budget/revenue cells. Using mean or a presumptive calculation to fill these cells might lead a false deduction.Only the rows which have revenue and budget used to overcome this difficulty and to get a precise result;however, results may change in the completed data.\n",
    "\n",
    "**2.** There are some missing director names in the columns. Because of these missing names, director rates weren't calculated and presented. Directors have a leading role for the creation of a popular, high revenue and rated movies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "*https://stackoverflow.com/questions/4018167/how-to-set-number-of-digits-for-float-point-output-in-ipython/6439856*\n",
    "\n",
    "*www.udacity.com*\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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